Agent-Based Modeling, ABM
Version 1.0.0 · Updated 2026-07-30
CORE DEFINITION
Instead of using macroscopic equations to describe the system, it simulates thousands of micro-level individuals (agents) and their simple interaction rules. By observing the interactions of these individuals, macroscopic phenomena (such as traffic jams, stock market crashes) emerge from the bottom up. Scaffold role: macro-level deduction from micro-level interactions. When predicting complex phenomena (such as virus spread, rumor diffusion), ABM is more accurate than macro formulas. It restores the granularity of the world by simulating 'each individual's choices'.
SCAFFOLDING EFFECT
Reduce cognitive load
Macro-level deduction from micro-level interactions. When predicting complex phenomena (such as virus spread, rumor diffusion), ABM is more accurate than macro formulas. It restores the granularity of the world by simulating 'each individual's choices'.
Anchor fast decisions
Bottom-up modeling: each agent has states and rules, updated according to local interactions (neighbors, environment); macro patterns emerge from micro interactions, without pre-writing macro equations.
MINIMUM ACTION
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Source support: Explicit
- zh.wikipedia.orghttps://zh.wikipedia.org/wiki/%E4%B8%AA%E4%BD%93%E4%B8%BA%E6%9C%AC%E6%A8%A1%E5%9E%8Bverified
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